Integrative analysis of TROAP with molecular features, carcinogenesis, and related immune and pharmacogenomic characteristics in soft tissue sarcoma.

Soft tissue sarcoma (STS) is an uncommon malignancy that often carries a grim prognosis. Trophinin-associated protein (TROAP) is augmented in a variety of tumors and can affect tumor proliferation. Nevertheless, the prognostic value and specific functions of TROAP in STS are still vague. Herein, we display that TROAP exhibits an augmented trend in STS, and its elevation correlates with a poor prognosis of STS. Furthermore, its reduction is related to increased immune cell infiltration, enhanced stroma, and elevation of immune activation. Meanwhile, the TROAP-derived genomic signature is validated to predict patient prognosis, immunotherapy, and drug response reliably. A nomogram constructed based on age, metastatic status, and a TROAP-derived risk score of an STS individual could be used to quantify the survival probability of STS. In addition, in vitro experiments have demonstrated that TROAP is overexpressed in STS, and the downregulation of TROAP could affect the proliferation, migration, metastasis, and cell cycle of STS cells. In summary, the TROAP expression is elevated in STS tissues and cells, which is related to the poor prognosis and malignant biological behaviors of STS. It could act as a potential prognostic biomarker for diagnosis and treatment of STS.

Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma.

Background: Soft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS. Methods: The ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis. Results: A novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells. Conclusion: In sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

Integration analysis based on fatty acid metabolism robustly predicts prognosis, dissecting immunity microenvironment and aiding immunotherapy for soft tissue sarcoma.

Background: Soft tissue sarcoma (STS) is a highly malignant tumor with a dismal prognosis. Presently, the dysregulation of fatty acid metabolism has received increasing attention in tumor research, but fewer reports are relevant to STS. Methods: Based on fatty acid metabolism-related genes (FRGs), a novel risk score for STS was developed utilizing univariate analysis and least absolute shrinkage selection operator (LASSO) Cox regression analyses in the STS cohort, which were further validated using the external validation cohort from other databases. Furthermore, independent prognostic analysis, C-index, ROC curves, and nomogram were carried out to investigate the predictive performance of fatty acid-related risk scores. We also analysed the differences in enrichment pathways, the immune microenvironment, gene mutations, and immunotherapy response between the two distinct fatty acid score groups. Moreover, the real-time quantitative polymerase chain reaction (RT-qPCR) was used to further verify the expression of FRGs in STS. Results: A total of 153 FRGs were retrieved in our study. Next, a novel fatty acid metabolism-related risk score (FAS) was constructed based on 18 FRGs. The predictive performance of FAS was also verified in external cohorts. In addition, the independent analysis, C-index, ROC curve, and nomograph also revealed that FAS could serve as an independent prognostic factor for the STS patients. Meanwhile, our results demonstrated that the STS cohort in two distinct FAS groups had different copy number variations, immune cell infiltration, and immunotherapy responses. Finally, the in vitro validation results demonstrated that several FRGs included in the FAS exhibited abnormal expression in STS. Conclusion: Altogether, our work comprehensively and systematically clarifies fatty acid metabolism's potential roles and clinical significance in STS. The novel individualized score based on fatty acid metabolism may be provided as a potential marker and treatment strategy in STS.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers.

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning.

Background: Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS. Methods: We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR). Results: Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS. Conclusion: In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

Comprehensive analysis of a novel cuproptosis-related lncRNA signature associated with prognosis and tumor matrix features to predict immunotherapy in soft tissue carcinoma.

Background: A crucial part of the malignant processes of soft tissue sarcoma (STS) is played by cuproptosis and lncRNAs. However, the connection between cuproptosis-related lncRNAs (CRLs) and STS is nevertheless unclear. As a result, our objective was to look into the immunological activity, clinical significance, and predictive accuracy of CRLs in STS. Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively, provided information on the expression patterns of STS patients and the general population. Cuproptosis-related lncRNA signature (CRLncSig) construction involved the univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analysis. The predictive performance of the CRLncSig was evaluated using a serial analysis. Further research was done on the connections between the CRLncSig and the tumor immune milieu, somatic mutation, immunotherapy response, and chemotherapeutic drug susceptibility. Notably, an in vitro investigation served to finally validate the expression of the hallmark CRLs. Results: A novel efficient CRLncSig composed of seven CRLs was successfully constructed. Additionally, the low-CRLncSig group's prognosis was better than that of the high-CRLncSig group's based on the new CRLncSig. The innovative CRLncSig then demonstrated outstanding, consistent, and independent prognostic and predictive usefulness for patients with STS, according to the evaluation and validation data. The low-CRLncSig group's patients also displayed improved immunoreactivity phenotype, increased immune infiltration abundance and checkpoint expression, and superior immunotherapy response, whereas those in the high-CRLncSig group with worse immune status, increased tumor stemness, and higher collagen levels in the extracellular matrix. Additionally, there is a noticeable disparity in the sensitivity of widely used anti-cancer drugs amongst various populations. What's more, the nomogram constructed based on CRLncSig and clinical characteristics of patients also showed good predictive ability. Importantly, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) demonstrated that the signature CRLs exhibited a significantly differential expression level in STS cell lines. Conclusion: In summary, this study revealed the novel CRLncSig could be used as a promising predictor for prognosis prediction, immune activity, tumor immune microenvironment, immune response, and chemotherapeutic drug susceptibility in patients with STS. This may provide an important direction for the clinical decision-making and personalized therapy of STS.

Molecular characterization of immunogenic cell death indicates prognosis and tumor microenvironment infiltration in osteosarcoma.

Introduction: Osteosarcoma (OS) is a highly aggressive bone malignancy with a poor prognosis, mainly in children and adolescents. Immunogenic cell death (ICD) is classified as a type of programmed cell death associated with the tumor immune microenvironment, prognosis, and immunotherapy. However, the feature of the ICD molecular subtype and the related tumor microenvironment (TME) and immune cell infiltration has not been carefully investigated in OS. Methods: The ICD-related genes were extracted from previous studies, and the RNA expression profiles and corresponding data of OS were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. The ICD-related molecular subtypes were classed by the "ConsensusclusterPlus" package and the construction of ICD-related signatures through univariate regression analysis. ROC curves, independent analysis, and internal validation were used to evaluate signature performance. Moreover, a series of bioinformatic analyses were used for Immunotherapy efficacy, tumor immune microenvironments, and chemotherapeutic drug sensitivity between the high- and low-risk groups. Results: Herein, we identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune response signaling among distinct ICD subtypes. Subsequently, a novel ICD-related prognostic signature was developed to determine its predictive performance in OS. Also, a highly accurate nomogram was then constructed to improve the clinical applicability of the novel ICD-related signature. Furthermore, we observed significant correlations between ICD risk score and TME, immunotherapy response, and chemotherapeutic drug sensitivity. Notably, the in vitro experiments further verified that high GALNT14 expression is closely associated with poor prognosis and malignant progress of OS. Discussion: Hence, we identified and validated that the novel ICD-related signature could serve as a promising biomarker for the OS's prognosis, chemotherapy, and immunotherapy response prediction, providing guidance for personalized and accurate immunotherapy strategies for OS.

The emerging role of pyroptosis in pediatric cancers: from mechanism to therapy.

Pediatric cancers are the driving cause of death for children and adolescents. Due to safety requirements and considerations, treatment strategies and drugs for pediatric cancers have been so far scarcely studied. It is well known that tumor cells tend to progressively evade cell death pathways, which is known as apoptosis resistance, one of the hallmarks of cancer, dominating tumor drug resistance. Recently, treatments targeting nonapoptotic cell death have drawn great attention. Pyroptosis, a newly specialized form of cell death, acts as a critical physiological regulator in inflammatory reaction, cell development, tissue homeostasis and stress response. The action in different forms of pyroptosis is of great significance in the therapy of pediatric cancers. Pyroptosis could be induced and consequently modulate tumorigenesis, progression, and metastasis if treated with local or systemic therapies. However, excessive or uncontrolled cell death might lead to tissue damage, acute inflammation, or even cytokine release syndrome, which facilitates tumor progression or recurrence. Herein, we aimed to describe the molecular mechanisms of pyroptosis, to highlight and discuss the challenges and opportunities for activating pyroptosis pathways through various oncologic therapies in multiple pediatric neoplasms, including osteosarcoma, neuroblastoma, leukemia, lymphoma, and brain tumors.

Identification of cuproptosis-related lncRNA prognostic signature for osteosarcoma.

Background: Copper is an indispensably mineral element involved in various metabolic processes and functions in the active sites of many metalloproteins. Copper dysregulation is associated with cancers such as osteosarcoma (OS), the most common primary bone malignancy with invasiveness and metastasis. However, the causality between cuproptosis and OS remains elusive. We aim to identify cuproptosis-related long non-coding RNAs (lncRNAs) for osteosarcomatous prognosis, immune microenvironment response, and immunotherapy. Methods: The Person correlation and differential expression analysis were used to identify differentially expressed cuproptosis-related lncRNAs (CRLs). The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were performed to construct the CRL signature. The Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) curve, internal validation, independent prognostic analysis, and nomograph were used to evaluate the prognostic value. The functional enrichment, tumor microenvironment, immunotherapy and chemotherapy response between the two distinct groups were further explored using a series of algorithms. The expression of signature CRLs was verified by real-time quantitative polymerase chain reaction (RT-qPCR) in OS cell lines. Results: A novel CRL signature consisting of four CRLs were successfully identified. The K-M survival analysis indicated that the OS patients in the low-risk groups had a better prognosis than that in the high-risk group. Then, the ROC curve and subgroup survival analysis confirmed the prognostic evaluation performance of the signature. Equally, the independent prognostic analysis demonstrated that the CRL signature was an independently predicted factor for OS. Friends analysis determined the hub genes that played a critical role in differentially expressed genes between two distinct risk groups. In addition, the risk score was related to immunity status, immunotherapy response, and chemotherapeutic drug sensitivity. Finally, the expression of these signature CRLs detected by RT-qPCR was consistent with the bioinformatic analysis results. Conclusion: In summary, our study confirmed that the novel CRL signature could effectively evaluate prognosis, tumor immune microenvironment, and immunotherapy response in OS. It may benefit for clinical decision-making and provide new insights for personalized therapeutics.

A novel necroptosis-related lncRNAs signature effectively predicts the prognosis for osteosarcoma and is associated with immunity.

Background: Necroptosis is closely related to tumorigenesis and development. Accumulating evidence has revealed that long non-coding RNAs (lncRNAs) are also central players in osteosarcoma (OS). However, the role of necroptosis-related lncRNAs in OS remains unclear. In the present study, we aim to craft a prognostic signature based on necroptosis-related lncRNAs to improve the OS prognosis prediction. Methods: The signature based on necroptosis-related lncRNAs was discovered using univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The prognosis efficiency of the signature was then estimated by employing various bioinformatics methods. Subsequently, immunological analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the association between necroptosis-related lncRNAs with clinical outcomes and immune status. More importantly, several necroptosis-related lncRNAs were validated with RT-qPCR. Results: Consequently, a novel prognosis signature was successfully constructed based on eight necroptosis-related lncRNAs. Meanwhile, the novel necroptosis-related lncRNAs model could distribute OS patients into two risk groups with a stable and accurate predictive ability. Additionally, the GSEA and immune analysis revealed that the necroptosis-related lncRNAs signature affects the development and prognosis of OS by regulating the immune status. The necroptosis-related lncRNA signature was closely correlated with multiple anticancer agent susceptibility. Moreover, the RT-qPCR results indicated several necroptosis-related lncRNAs were significantly differently expressed in osteosarcoma and osteoblast cell lines. Conclusion: In this summary, a novel prognostic signature integrating necroptosis-related lncRNAs was firstly constructed and could accurately predict the prognosis of OS. This study may increase the predicted value and guide the personalized chemotherapy treatment for OS.

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study.

Background: As a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends. Methods: We screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications. Results: A total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis. Conclusion: Publications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

A Necroptosis-Related lncRNA Signature Predicts Prognosis and Indicates the Immune Microenvironment in Soft Tissue Sarcomas.

Background: The necroptosis and long noncoding RNA (lncRNA) are critical in the occurrence and development of malignancy, while the association between the necroptosis-related lncRNAs (NRlncRNAs) and soft tissue sarcoma (STS) remains controversial. Therefore, the present study aims to construct a novel signature based on NRlncRNAs to predict the prognosis of STS patients and investigate its possible role. Methods: The transcriptome data and clinical characteristics were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression database (GTEx). A novel NRlncRNA signature was established and verified by the COX regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the K-M survival analysis, ROC, univariate, multivariate Cox regression analysis, and nomogram were used to evaluate the predictive value of the signature. Also, a variety of bioinformatic analysis algorithms explored the differences between the potential mechanism, tumor immune status, and drug sensitivity in the two-risk group. Finally, the RT-qPCR was performed to evaluate the expression of signature NRlncRNAs. Results: A novel signature consisting of seven NRlncRNAs was successfully established and verified with stable prediction performance and general applicability for STS. Next, the GSEA showed that the patients in the high-risk group were mainly enriched with tumor-related pathways, while the low-risk patients were significantly involved in immune-related pathways. In parallel, we found that the STS patients in the low-risk group had a better immune status than that in the high-risk group. Additionally, there were significant differences in the sensitivity to anti-tumor agents between the two groups. Finally, the RT-qPCR results indicated that these signature NRlncRNAs were abnormally expressed in STS. Conclusion: To the best of our knowledge, it is the first study to construct an NRlncRNA signature for STS. More importantly, the novel signature displays stable value and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in STS.

Exosomal MiRNAs in Osteosarcoma: Biogenesis and Biological Functions.

MiRNAs are a group of non-coding RNA molecules that function in mRNA translational inhibition via base-pairing with complementary sequences in target mRNA. In oncology, miRNAs have raised great attention due to their aberrant expression and pivotal roles in the pathogenesis of multiple malignancies including osteosarcoma. MiRNAs can be transported by exosome, the nano-extracellular vesicle with a diameter of 30-150 nm. Recently, a growing number of studies have demonstrated that exosomal miRNAs play a critical role in tumor initiation and progression, by exerting multiple biological functions including metastasis, angiogenesis, drug resistance and immunosuppression. In this review, we aim to depict the biogenesis of exosomal miRNAs and summarize the potential diagnostic and therapeutic functions of exosomal miRNAs in osteosarcoma.

Kinematic alignment versus mechanical alignment in primary total knee arthroplasty: an updated meta-analysis of randomized controlled trials.

BACKGROUND: The purpose of this study was to perform an updated meta-analysis to compare the outcomes of kinematic alignment (KA) and mechanical alignment (MA) in patients undergoing total knee arthroplasty. METHODS: PubMed, EMBASE, Web of Science, Google Scholar, and the Cochrane Library were systematically searched. Eligible randomized controlled trials regarding the clinical outcomes of patients undergoing total knee arthroplasty with KA and MA were included for the analysis. RESULTS: A total of 1112 participants were included in this study, including 559 participants with KA and 553 patients with MA. This study revealed that the Western Ontario and McMaster Universities Osteoarthritis Index, Knee Society Score (knee and combined), and knee flexion range were better in the patients with kinematic alignment than in the mechanical alignment. In terms of radiological results, the femoral knee angle, mechanical medial proximal tibial angle, and joint line orientation angle were significantly different between the two techniques. Perioperatively, the walk distance before discharge was longer in the KA group than in the MA group. In contrast, other functional outcomes, radiological results, perioperative outcomes, and postoperative complication rates were similar in both the kinematic and mechanical alignment groups. CONCLUSIONS: The KA technique achieved better functional outcomes than the mechanical technique in terms of KSS (knee and combined), WOMAC scores, and knee flexion range. PROSPERO trial registration number CRD42021264519. Date registration: July 28, 2021.

MTHFR Polymorphism Is Associated With Severe Methotrexate-Induced Toxicity in Osteosarcoma Treatment.

BACKGROUND: Previous studies have revealed the critical role of methylene tetrahydrofolate reductase (MTHFR) polymorphisms in response to high-dose methotrexate (MTX)-induced toxicity in osteosarcoma patients. However, the conclusions remain controversial. In this setting, we performed a meta-analysis to determine their association more precisely. METHOD: Eligible studies were searched and screened in PubMed, Web of Science, Cochrane Library, Clinical-Trials.gov, Embase, and China National Knowledge Infrastructure (CNKI) following specific inclusion and exclusion criteria. The required information was retrieved and collected for subsequent meta-analysis. Association between MTHFR polymorphism and MTX toxicity was evaluated by odds ratios (ORs). RESULTS: Seven studies containing 585 patients were enrolled and analyzed in this meta-analysis. Overall, the MTX related grade 3-4 liver toxicity was significantly associated with MTHFR rs1801133 allele (T vs. C: OR=1.61, 95%CI=1.07-2.42, P=0.024), homozygote (TT vs. CC: OR=2.11, 95%CI=1.06-4.21, P=0.011), and dominant genetic model (TT/TC vs. CC: OR=3.15, 95%CI=1.30-7.60, P=0.035) in Asian population. Meanwhile, close associations between MTX mediated grade 3-4 mucositis and MTHFR rs1801133 polymorphism were identified in allele contrast (T vs. C: OR=2.28, 95%CI=1.49-3.50, P<0.001), homozygote comparison (TT vs. CC: OR=4.07, 95%CI=1.76-9.38, P=0.001), heterozygote comparison (TC vs. CC: OR=2.55, 95%CI=1.20-5.42, P=0.015), recessive genetic model (TT vs. TC/CC: OR=2.09, 95%CI=1.19-3.67, P=0.010), and dominant genetic model (TT/TC vs. CC: OR=2.97, 95%CI=1.48-5.96, P=0.002). Additionally, kidney toxicity was corelated with the heterozygote comparison (TC vs. CC: OR=2.63, 95%CI=1.31-5.29, P=0.007) of rs1801133 polymorphism. CONCLUSION: The MTHFR rs1801133 polymorphism was significantly associated with severer liver toxicity induced by high-dose MTX treatment in the Asian population. In the meantime, patients with MTHFR rs1801133 polymorphism were predisposed to MTX- related mucositis.